Abstract PO-099: African American women with breast cancer have unique molecular features and reduced clinical trial enrollment
Bibliographic record
Abstract
Abstract Introduction: African American (AA) women have a lower incidence of breast cancer (BC), however experience higher rates of more aggressive subtypes, mortality, and worse outcomes compared to Caucasian (C) women. Data also points to disparities among treatment access and response in AA women. Most existing genetic research and clinical trials have been conducted with majority C women, thus our existing knowledge about molecular profiles and effective treatments may not be accurate for AA women. The aim of this study is to uncover the biological and social factors related to treatment that may be causing this disparity in BC outcomes between AA and C women. Materials and Methods: Data from Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas Program (TCGA) was analyzed for differences in etiology, incidence, and prevalence of BC between AA and C. Prevalence of molecular aberrations and subtypes of BC were assessed to gain insight into potential causes of this disparity. FDA's Drug Trials Snapshots data was assessed for clinical trial enrollment by race. Results: SEER data from 2000-2018 reveals AA patients have higher incidences of TNBC and HER2 enriched BC compared to C, with luminal A incidence being higher in C patients. Within all subtypes of BC, 5-year survival rates were significantly lower for AA compared to C. TCGA data found 2282 genes that were significantly differentially altered in C vs. AA subgroups. Among the most commonly altered and significantly different genes, CSMD1 and TP53 were more prevalent among AA (19.78% AA vs. 10.79% in C for CSMD1, and 40.09% AA vs. 28.58% C for TP53), and PIK3CA among C (36.28% (C) vs. 22.47% (AA)). Survival analysis revealed AA women with PIK3CA alterations had lower rates of disease-free survival compared to C women. Clinical trials for oncology drugs approved in 2020 enrolled 5% AAs, with AAs also being underrepresented for BC specific drugs. The recent NCT02492711 trial of MARGENZA, for metastatic HER2 positive BC, had 5% AA compared to 80% C, the NCT01631552 trial of TRODELVY, for metastatic triple negative BC (TNBC), had 7% AA vs. 76% C, and the NCT02614794 trial of TUKYSA, for advanced HER2+ BC, had 9% AA vs. 73% C. Conclusion: Most existing treatments are for luminal A BC; with fewer approved drugs for subtypes more common among AA women, including the more aggressive TNBC. Recent data indicates that AA women continue to be underrepresented in clinical trials, despite having higher incidence of both HER2 and TNBC, resulting in a poor understanding of effective treatment for this subgroup. These factors may perpetuate disparities in outcomes for AA women with BC. Additionally, TCGA data uncovered underlying molecular differences in BC between AA and C women, some that are actionable. Disparities are multifactorial and can include both biological and socioeconomic factors. Thus, as we move towards more personalized medicine, it is increasingly important to improve representation of AA women in precision oncology trials. Citation Format: Genevra Magliocco, Roy Khalife, Anthony Magliocco. African American women with breast cancer have unique molecular features and reduced clinical trial enrollment [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-099.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".